1. Field of the Invention
The present invention is related to a pedestrian detection system and method, and more particularly, to a pedestrian detection system and system applying histogram of gradient of granule feature of an image to enhance the pedestrian detection rate and detection precision.
2. Description of the Related Art
There is a variety of imaging driving safety devices applying in the pedestrian detection nowadays. These safety devices with different designs and manufacture processes can generate different results.
Traditionally, the existing technology uses template matching to achieve the purpose of detecting the pedestrian. The existing technology mainly constructs human form templates or modules with different angles and attitudes to compare with the detection image so as to achieve the pedestrian detection. For the features of the appearance of human contour, the existing technology adopts the silhouette or the edge image to represent the human contour, and converts the silhouette or the edge image to a distance transform image. In order to overcome the translation, scale and orientation variations of the object more effectively, it develops the human contour feature picture containing wavelet coefficients. Additionally, histogram of oriented gradients (HOG) is used to represent the feature of human contour, and the machine learning method is used as the core of the imaging pedestrian detection to perform the identification and the classification by the support vector machine (SVM) so as to effectively identify the pedestrian or non-pedestrian.
Therefore, HOG can better overcome the variation of the human contour to achieve the better detection result. The calculation of HOG is to divide the image to a plurality of blocks, and then count the amount of magnitude of the pixel gradient in any orientation in each block so as to form a histogram of oriented gradients (HOG). HOG has a strong description capability for the edge information, and also adapts to the edge shift and the slightly rotation due to the counting calculation.
However, HOG lacks the texture information because of the property of the counting calculation. For example, HOG can not effectively identify a single complete line or complicate lines. Thereby, HOG will make mistake when the human appears in a clutter environment.
Accordingly, a system and method for solving the aforementioned problems is needed.